AIMC Topic: Deep Learning

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Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms.

Proceedings. Biological sciences
The question of whether task performance is best achieved by domain-specific, or domain-general processing mechanisms is fundemental for both artificial and biological systems. This question has generated a fierce debate in the study of expert object...

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods...

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.

European radiology
OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and qua...

A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images.

Artificial intelligence in medicine
Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their applicati...

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.

Multiple sclerosis and related disorders
Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we d...

High-throughput deep learning variant effect prediction with Sequence UNET.

Genome biology
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult t...

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Respiratory research
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operat...

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.

BMC bioinformatics
BACKGROUND: An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensem...

Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification.

BMC nephrology
BACKGROUND: Electron microscopy is important in the diagnosis of renal disease. For immune-mediated renal disease diagnosis, whether the electron-dense granule is present in the electron microscope image is of vital importance. Deep learning methods ...

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging.

Scientific reports
Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dys...